Optical proximity correction (OPC) is a photolithography enhancement technique commonly used to compensate for image errors due to diffraction or process effects. OPC is often used for the fabrication of semiconductor devices due to the limitations of light to maintain the edge placement integrity of the original design, after processing, into the etched image on the silicon wafer. The projected images tend to appear with irregularities such as line widths that are narrower or wider than designed, and such irregularities are amenable to compensation by changing the pattern on the photomask used for imaging. Other distortions such as rounded corners are driven by the resolution of the optical imaging tool and are harder to compensate for. Such distortions, if not corrected for, may significantly alter the electrical properties of what was being fabricated. OPC corrects these errors by moving edges or adding extra polygons to the pattern written on the photomask. This may be driven by pre-computed look-up tables based on width and spacing between features (known as rule based OPC) or by using compact models to dynamically simulate the final pattern and thereby drive the movement of edges, typically broken into sections, to find the best solution, (this is known as model based OPC). The objective is to reproduce, as well as possible, the original layout drawn by the designer in the silicon wafer. Thus, OPC model accuracy is critical for advanced nodes.
Presently there are generally two approaches to quantifying OPC model accuracy, namely the physical approach and the simulation approach. Under the physical approach, empirical data from the wet and dry systems are fitted to models which are generated with specific numerical aperture (NA), source shape and exposure systems. Ambient components, such as air and water at a certain refractive index (e.g., 1.43), are transposed along with the measured empirical data to confirm the model accuracy. Under the simulation approach, model accuracy depends on several factors, primarily the intrinsic ability to represent the patterning trends through target size, pitch, and pattern shape for one-dimensional and two-dimensional structures at a given process condition. Also, calibration test pattern design coverage is important whenever model accuracy is in question, and this tends to be a problem. Further, root mean square (RMS) metric is used for simulation.